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Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #308578
Title: Estimating Effect of Time-Dependent Treatment in Observational Studies
Author(s): Pallavi Mishra-Kalyani*+ and Brent Johnson and Qi Long
Companies: Emory University, Department of Biostatistics and Emory University, Department of Biostatistics and Emory University, Department of Biostatistics
Keywords: Missing data ; Principal Stratification ; Censoring by death ; Non-random treatment ; Propensity function ; Bayesian Inference
Abstract:

Observational studies such as patient registries often pose unique challenges that require special care in statistical analysis. Our current paper is motivated by an amyotrophic lateral sclerosis (ALS) registry, where the receipt of treatment is time-dependent and both the receipt of treatment and/or outcome are subject to "censoring" by death. The analysis of the ALS registry is further complicated by non-random treatment assignment, time-dependent covariates, and missing data. Extending the work of Zhang et al (2009), we aim to estimate the effect of time to treatment within principal strata defined by survival status at the time of outcome measurement. We develop a Bayesian modeling approach to estimate the treatment effect while correcting for potential bias induced by non-random treatment assignment and by missing data. The proposed approach is evaluated in simulation studies and is further illustrated using data from the ALS registry.


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